Thinking, Fast and Slow

Nonregressive Intuitions
Let us return to a person we have already met:
Julie is currently a senior in a state university. She read fluently when she was four
years old. What is her grade point average (GPA)?
People who are familiar with the American educational scene quickly come up with a
number, which is often in the vicinity of 3.7 or 3.8. How does this occur? Several
operations of System 1 are involved.

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A causal link between the evidence (Julie’s reading) and the target of the prediction
(her GPA) is sought. The link can be indirect. In this instance, early reading and a
high GDP are both indications of academic talent. Some connection is necessary. You
(your System 2) would probably reject as irrelevant a report of Julie winning a fly
fishing competitiowhired D=n or excelling at weight lifting in high school. The
process is effectively dichotomous. We are capable of rejecting information as
irrelevant or false, but adjusting for smaller weaknesses in the evidence is not
something that System 1 can do. As a result, intuitive predictions are almost
completely insensitive to the actual predictive quality of the evidence. When a link is
found, as in the case of Julie’s early reading, WY SIATI applies: your associative
memory quickly and automatically constructs the best possible story from the
information available.
Next, the evidence is evaluated in relation to a relevant norm. How precocious is a
child who reads fluently at age four? What relative rank or percentile score
corresponds to this achievement? The group to which the child is compared (we call
it a reference group) is not fully specified, but this is also the rule in normal speech: if
someone graduating from college is described as “quite clever” you rarely need to
ask, “When you say ‘quite clever,’ which reference group do you have in mind?”
The next step involves substitution and intensity matching. The evaluation of the
flimsy evidence of cognitive ability in childhood is substituted as an answer to the
question about her college GPA. Julie will be assigned the same percentile score for
her GPA and for her achievements as an early reader.
The question specified that the answer must be on the GPA scale, which requires
another intensity-matching operation, from a general impression of Julie’s academic
achievements to the GPA that matches the evidence for her talent. The final step is a
translation, from an impression of Julie’s relative academic standing to the GPA that
corresponds to it.
Intensity matching yields predictions that are as extreme as the evidence on which
they are based, leading people to give the same answer to two quite different questions:
What is Julie’s percentile score on reading precocity?
What is Julie’s percentile score on GPA?
By now you should easily recognize that all these operations are features of System 1.
I listed them here as an orderly sequence of steps, but of course the spread of activation in
associative memory does not work this way. You should imagine a process of spreading
activation that is initially prompted by the evidence and the question, feeds back upon
itself, and eventually settles on the most coherent solution possible.

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Amos and I once asked participants in an experiment to judge descriptions of eight college
freshmen, allegedly written by a counselor on the basis of interviews of the entering class.
Each description consisted of five adjectives, as in the following example:
intelligent, self-confident, well-read, hardworking, inquisitive
We asked some participants to answer two questions:
How much does this description impress you with respect to academic ability?
What percentage of descriptions of freshmen do you believe would impress you
more?
The questions require you to evaluate the evidence by comparing the description to
your norm for descriptions of students by counselors. The very existence of such a norm is
remarkable. Although you surely do not know how you acquired it, you have a fairly clear
sense of how much enthusiasm the description conveys: the counselor believes that this
student is good, but not spectacularly good. There is room for stronger adjectives than
intelligent
(
brilliant
, 
creative
), 
well-read
(
scholarly, erudite, impressively knowledgeable
),
and 
hardworking
(
passionate
, 
perfectionist
). The verdict: very likely to be in the top 15%
but unlikely to be in the top 3%. There is impressive consensus in such judgments, at least
within a culture.
The other participants in our experiment were asked different questions:
What is your estimate of the grade point average that the student will obtain?
What is the percentage of freshmen who obtain a higher GPA?
You need another look to detect the subtle difference between the two sets of
questions. The difference should be obvious, but it is not. Unlike the first questions, which
required you only to evaluate the evidence, the second set involves a great deal of
uncertainty. The question refers to actual performance at the end of the freshman year.
What happened during the year since the interview was performed? How accurately can
you predict the student’s actual achievements in the first year at college from five
adjectives? Would the counselor herself be perfectly accurate if she predicted GPA from
an interview?
The objective of this study was to compare the percentile judgments that the

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participants made when evaluating the evidence in one case, and when predicting the
ultimate outcome in another. The results are easy to summarize: the judgments were
identical. Although the two sets of questions differ (one is about the description, the other
about the student’s future academic performance), the participants treated them as if they
were the same. As was the case with Julie, the prediction of the future is not distinguished
from an evaluation of current evidence—prediction matches evaluation. This is perhaps
the best evidence we have for the role of substitution. People are asked for a prediction but
they substitute an evaluation of the evidence, without noticing that the question they
answer is not the one they were asked. This process is guaranteed to generate predictions
that are systematically biased; they completely ignore regression to the mean.
During my military service in the Israeli Defense Forces, I spent some time attached
to a unit that selected candidates for officer training on the basis of a series of interviews
and field tests. The designated criterion for successful prediction was a cadet’s final grade
in officer school. The validity of the ratings was known to be rather poor (I will tell more
about it in a later chapter). The unit still existed years later, when I was a professor and
collaborating with Amos in the study of intuitive judgment. I had good contacts with the
people at the unit and asked them for a favor. In addition to the usual grading system they
used to evaluate the candidates, I asked for their best guess of the grade that each of the
future cadets would obtain in officer school. They collected a few hundred such forecasts.
The officers who had produced the prediof рctions were all familiar with the letter grading
system that the school applied to its cadets and the approximate proportions of A’s, B’s,
etc., among them. The results were striking: the relative frequency of A’s and B’s in the
predictions was almost identical to the frequencies in the final grades of the school.
These findings provide a compelling example of both substitution and intensity
matching. The officers who provided the predictions completely failed to discriminate
between two tasks:
their usual mission, which was to evaluate the performance of candidates during their
stay at the unit
the task I had asked them to perform, which was an actual prediction of a future
grade
They had simply translated their own grades onto the scale used in officer school,
applying intensity matching. Once again, the failure to address the (considerable)
uncertainty of their predictions had led them to predictions that were completely
nonregressive.